---
title: "Perspectives on Benefits and Harms"
output: 
  flexdashboard::flex_dashboard:
    storyboard: true
    theme: readable
    css: "style.css"
    social: menu
    source: embed
editor_options: 
  chunk_output_type: console
---

```{r setup, include=FALSE}
library(flexdashboard)
library(svglite)
library(dplyr)
devtools::load_all()
```

### Balancing benefits and harm

```{r}
knitr::include_graphics("www/balance.svg")
```

------------------------------------------------------------------------

Clinical trials and subsequent meta-analyses tend to include multiple outcomes
that have decision making implications. Generally we are interested in finding
interventions that maximize benefit while minimizing harm. There are many
methods that can be used to achieve this, but the most important consideration
often has nothing to do the final calculations used.

### Complex complications

```{r, fig.width= 8, dev = "svglite"}

 data("dat")



dat %>%
  dplyr::mutate(
    out = factor(out, levels = c("mort", "cp"), 
                 labels = c("Mortality", "Disability")
    )
  ) %>%
  ggplot2::ggplot(ggplot2::aes(y = point, ymin = lwr, ymax = upr, x = study)) +
  ggplot2::geom_pointrange(colour = "#375180", size = 1) +
  ggplot2::coord_flip() +
  ggplot2::facet_wrap(~out) +
  ggplot2:::geom_hline(yintercept = 1, linetype = 2) +
  ggplot2::theme_minimal(base_size = 16) +
  ggplot2::labs(
    x = "",
    y = "Odds Ratio (lower is better)",
    title = "Increased Harms Can Appear in Outcomes Downstream from Mortality Even If They Don't Exist at the Patient Level"
  ) +
  ggplot2::theme(
    axis.text.y = ggplot2::element_blank(),
    plot.title = ggtext::element_textbox_simple(hjust = 1, width = grid::unit(1, "npc"), height = grid::unit(0.2, "npc"))
  ) 

  
```

------------------------------------------------------------------------

A classic example of weighing benefits and harms in neonatology is the benefit
of post-natal steroids on death or BPD weighed against the increased risk of
cerebral palsy. The problem is that since infants who die can't develop cerebral
palsy, any treatment with a mortality benefit will appear to increase ceberal palsy risk. We can see that even when we simulate many trials with a benefit on mortality and no effect on
development, it still looks like the intervention may be associated with harm. 

### So what?

```{r }
knitr::include_graphics("www/downstream.svg")
```

------------------------------------------------------------------------

Comparing the total effect of treatment on mortality against cerebral palsy can make sense from a resource allocation standpoint: funders need to consider that an intervention that improves mortality might result in additional resource requirements for follow-up and early intervention. This is very different from
the problem faced by clinicians who want to make sure their intervention isn't
creating too much additional risk in an individual patient (the direct effect). A clinician who uses
these outputs to inform discussions with patients might inappropriately suggest
that the therapy may increase the risk of developmental delay, or alternatively
could present results of a composite (eg Death or disability) and say there is
no evidence of benefit.

### What can we do?

```{r}
knitr::include_graphics("www/collider.svg")
```

--- 
While it's tempting to try to the direct effect on disability doing
so would likely induce collider bias which will require careful consideration to address. The best way forward will depend on the particular context of a decision but a couple questions to consider might be:

1. Which perspective do you need? If you're making resource allocation decisions
then the total effect of intervention on downstream effects is likely relevant.

1. Which assumptions are you willing to make? If
you believe you can predict survival and all patients have a non zero probability of survival you may be able to get a reasonable estimate
of the direct effect of therapy on development.

1. How large is the effect on mortality? If mortality is rare or the effect is small then the total effect (or population level effect) and direct effect will be more
similar.
bias